GPT-6.1 Sol (effort default (no reasoning-effort override) · thinking on) on Animate a junction where cars obey a traffic light's timed sequence in one self-contained HTML file
OGPT-6.1 Soldefault · thinking on · Codex CLI 0.163.0 (codex exec --json) · cloud · 2026-10-23 · by @bobsiopener
- Estimated cost
- $0.03
- Wall time
- 52s
- Human step-ins
- 0
- Outcome
- Pass
Compare with other models on this task →
Summary
Exact prompt
# Animate a junction where cars obey a traffic light's timed sequence in one self-contained HTML file Make a small animated scene: a road junction with a traffic light. Write one file, `out/scene.html`, that works opened straight from disk with no network access. `spec.json` gives the page title, the stage size and the scene parameters. **Looks are not scored**: the page is opened in a headless browser (800x600 window, UTC time zone, network blocked) and checked by objective tests only. ## Scene contract - Draw the scene on one `<canvas>` or `<svg>` stage of `width` x `height` CSS pixels (spec.json), visible on load, with `<title>` containing spec.json `title`. - Animate continuously from load (`requestAnimationFrame` or timers); no click is needed unless stated below. - Expose `window.sceneState()`, returning the scene's current state in stage pixel coordinates (origin top-left of the stage): `{light: <the light showing now, one of the spec.json phase names>, cars: [{x}, ...]}`. Extra fields are fine. It must describe what is actually drawn. - Cars drive left to right along one lane; `x` is the front of the car in stage pixels and may wrap back to the left edge after a car leaves the stage. The light runs through `phases` in the order given, starting at the first phase on load, each for its `s` seconds, then repeats. Cars may only cross `stop_line_x` on green; on any other phase they stop at the line or queue behind the car ahead. Keep the traffic dense enough that a car is waiting at the line during the non-green phases. - The page must log no errors and throw no exceptions. Keep the file at most 200,000 bytes; no external scripts, fonts, images or URLs (inline everything). ## Checks (partial credit = share passed) 1. `self_contained`: `out/scene.html` exists, is at most 200,000 bytes, references no http(s) or protocol-relative URL, and requests nothing from the network when opened. 2. `has_canvas_or_svg`: a visible `<canvas>` or `<svg>` of at least 300x200 CSS pixels is on the page. 3. `frames_change`: over 3 s, some later screenshot differs from the first in at least 0.1% of pixels, and at least 3 of 30 screenshots differ. 4. `light_sequence`: over 3 s `sceneState().light` only takes spec.json phase names, changes only to the next phase in spec.json order (wrapping round), changes at least 3 times, and every phase seen from its start to its end lasts its `s` seconds within 0.25 s. 5. `cars_obey_lights`: `cars` has exactly spec.json `cars` entries; between two samples a car's x only goes from below `stop_line_x` to at or above it when the light is green in one of the two samples; some car moves while the light stays green, and some car waits (moves under 0.5 px) within 60 px before the stop line while the light stays on a phase other than green. 6. `no_console_errors`: no uncaught exception, `console.error` or browser error is logged during the run. ## How this run works - Work on your own: nobody will answer questions in this task, and writing to `ask/` counts against the run. - Write only under `out/`. Create `out/` if it is missing. Inputs are synthetic; don't fetch anything from the network. - Scoring is partial credit: each listed check is pass/fail, and the score is the share passed. Numbers are compared to within 0.005 unless the task says otherwise.
Original benchmark task written for usecaserank.com. Headless, offline run on a reproducible fixture. Cost shown is a list-price API-equivalent estimate (run on a ChatGPT subscription, not billed).
Success criteria: self_contained has_canvas_or_svg frames_change light_sequence cars_obey_lights no_console_errors
Setup notes: batch29 case h08-traffic-light-junction (offline fixture). Prepared with batch29/prepare.sh on the shared box; only TASK.md + starting files were copied to the agent machine, hidden key/tests stayed on the box. Prompt = TASK.md verbatim on stdin. Flags: --json -s workspace-write --skip-git-repo-check --ephemeral --ignore-user-config --ignore-rules -C <workdir>. Scored on the box with the scout's scorer: `MEDIA_DIR=<dir> python3 score.py WORKDIR KEYDIR`. Single run, no retries. User-level AGENTS.md (846 bytes, sha256 83ec4fe50da8) may have been in context; --ignore-user-config does not document excluding it. Autonomous case (seed 399810934): no stand-in user; any ask/ file counts as an intervention and blocks a pass. Serving model not confirmed: requested via -m; the event log does not echo the serving model id. Price source is secondary (pricing.json verification='secondary', https://www.actuia.com/en/news/ai-api-pricing-in-october-2026-050-to-50-for-output/), not an official OpenAI price page. Score: 6/6. Cost is an ESTIMATE: measured tokens x pricing.json list price (ChatGPT plan, nothing billed); input_tokens include cached reads; output_tokens include reasoning tokens. Tool calls 2 (0 failed, recovered). Single run.
Estimated cost List-price estimate; not actually billed.
Output
Metrics
- Estimated cost
- $0.03
- Wall time
- 52s
- Human step-ins
- 0
- Turns
- 1
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| OGPT-6.1 Solopenai/gpt-6.1-soleffort default (no reasoning-effort override) · thinking on | 48,392 | 1,371 | $0.03 |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: GPT-6.1 Sol (openai/gpt-6.1-sol, effort default (no reasoning-effort override) · thinking on) via
Codex CLI 0.163.0 (codex exec --json), cloud. - It counts as a success only if it meets every success criterion: self_contained has_canvas_or_svg frames_change light_sequence cars_obey_lights no_console_errors
- Log model, tokens, wall time, human step-ins and the evidence log, then submit it as a reproduction. A different model? Submit a head-to-head rerun.
How runs are scored: Methodology.
Was this useful?
Automated checks
Trust 80% comes from these checks (how Trust % works).
- Run date is not in the future : passed
- Model is in the dated pricing table : passed
- Cost recomputed from tokens matches : passed
- Cached tokens are consistent : passed
- Output throughput is plausible : passed
- Autonomy matches step-ins : passed
- Step-ins do not exceed turns : passed
- Retries agree with first-try result : passed
- Evidence log SHA-256 matches : passed
- Evidence log is substantial : passed
Evidence log
{"type": "thread.started", "thread_id": "01a16833-a1ed-7221-9106-77b0dc5e5933"}
{"type": "turn.started"}
{"type": "item.completed", "item": {"id": "item_0", "type": "agent_message", "text": "I\u2019ll read `spec.json` and build a self-contained canvas scene with timed lights and queued cars."}}
{"type": "item.started", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc 'cat spec.json'", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_1", "type": "command_execution", "command": "/bin/bash -lc 'cat spec.json'", "aggregated_output": "{\n \"title\": \"Junction 511\",\n \"width\": 720,\n \"height\": 480,\n \"file\": \"out/scene.html\",\n \"phases\": [\n {\n \"light\": \"red\",\n \"s\": 0.8\n },\n {\n \"light\": \"red_amber\",\n \"s\": 0.4\n },\n {\n \"light\": \"green\",\n \"s\": 0.8\n },\n {\n \"light\": \"amber\",\n \"s\": 0.4\n }\n ],\n \"cars\": 3,\n \"stop_line_x\": 325\n}", "exit_code": 0, "status": "completed"}}
{"type": "item.started", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc \"mkdir -p out\ncat > out/scene.html <<'EOF'\n<\"'!doctype html>\n<html lang=\"en\"><head><meta charset=\"utf-8\"><title>Junction 511</title><style>\nhtml,body{margin:0;background:#e3e9e5}canvas{display:block;width:720px;height:480px}\n</style></head><body><canvas id=\"stage\" width=\"720\" height=\"480\" aria-label=\"Animated traffic junction\"></canvas><script>\n'\"'use strict';\nconst canvas=document.getElementById('stage'),ctx=canvas.getContext('2d');\nconst phases=[{light:'red',s:.8},{light:'red_amber',s:.4},{light:'green',s:.8},{light:'amber',s:.4}];\nconst cars=[{x:324,color:'#3186d6'},{x:264,color:'#d65d49'},{x:204,color:'#e5ba42'}];\nconst start=performance.now();let simulated=0,light='red';\nfunction phaseAt(t){let p=((t%2.4)+2.4)%2.4;for(const phase of phases){if(p<phase.s-1e-10)return phase.light;p-=phase.s;}return 'red';}\nfunction advance(now){const target=(now-start)/1000;while(simulated<target){const dt=Math.min(1/240,target-simulated);const active=phaseAt(simulated+dt/2);const ordered=cars.slice().sort((a,b)=>b.x-a.x);for(let i=0;i<ordered.length;i++){const car=ordered[i];let limit=Infinity;if(car.x<325&&active\"'!=='\"'green')limit=324;if(i>0)limit=Math.min(limit,ordered[i-1].x-60);car.x=Math.max(car.x,Math.min(car.x+125*dt,limit));}for(const car of cars)if(car.x>780)car.x=-45;simulated+=dt;}light=phaseAt(target);}\nfunction draw(){ctx.clearRect(0,0,720,480);ctx.fillStyle='#86a776';ctx.fillRect(0,0,720,480);ctx.fillStyle='#51575b';ctx.fillRect(0,210,720,120);ctx.fillRect(355,0,110,480);ctx.strokeStyle='#d7d8c8';ctx.lineWidth=3;ctx.setLineDash([22,18]);ctx.beginPath();ctx.moveTo(0,270);ctx.lineTo(720,270);ctx.stroke();ctx.setLineDash([]);ctx.fillStyle='#fff';ctx.fillRect(325,275,4,49);ctx.fillStyle='#353a3c';ctx.fillRect(332,130,7,78);ctx.fillStyle='#20262b';ctx.fillRect(315,66,40,106);for(let i=0;i<3;i++){const on=i===0?(light==='red'||light==='red_amber'):i===1?(light==='amber'||light==='red_amber'):light==='green';ctx.beginPath();ctx.arc(335,85+i*33,12,0,Math.PI*2);ctx.fillStyle=on?['#ff3838','#ffc531','#38f06b'][i]:'#41484a';ctx.fill();}for(const car of cars){ctx.fillStyle='#20252a';ctx.fillRect(car.x-38,283,10,34);ctx.fillRect(car.x-14,283,9,34);ctx.fillStyle=car.color;ctx.fillRect(car.x-44,286,44,28);ctx.fillStyle='#c4e4ec';ctx.fillRect(car.x-16,289,9,22);ctx.fillStyle='#fff3c2';ctx.fillRect(car.x-3,289,3,5);ctx.fillRect(car.x-3,306,3,5);}ctx.fillStyle='#20372c';ctx.font='18px sans-serif';ctx.fillText('Junction 511',24,36);}\nwindow.sceneState=function(){advance(performance.now());draw();return {light,cars:cars.map(car=>({x:car.x}))};};\nfunction frame(now){advance(now);draw();requestAnimationFrame(frame);}draw();requestAnimationFrame(frame);\n</script></body></html>\nEOF\nwc -c out/scene.html\"", "aggregated_output": "", "exit_code": null, "status": "in_progress"}}
{"type": "item.completed", "item": {"id": "item_2", "type": "command_execution", "command": "/bin/bash -lc \"mkdir -p out\ncat > out/scene.html <<'EOF'\n<\"'!doctype html>\n<html lang=\"en\"><head><meta charset=\"utf-8\"><title>Junction 511</title><style>\nhtml,body{margin:0;background:#e3e9e5}canvas{display:block;width:720px;height:480px}\n</style></head><body><canvas id=\"stage\" width=\"720\" height=\"480\" aria-label=\"Animated traffic junction\"></canvas><script>\n'\"'use strict';\nconst canvas=document.getElementById('stage'),ctx=canvas.getContext('2d');\nconst phases=[{light:'red',s:.8},{light:'red_amber',s:.4},{light:'green',s:.8},{light:'amber',s:.4}];\nconst cars=[{x:324,color:'#3186d6'},{x:264,color:'#d65d49'},{x:204,color:'#e5ba42'}];\nconst start=performance.now();let simulated=0,light='red';\nfunction phaseAt(t){let p=((t%2.4)+2.4)%2.4;for(const phase of phases){if(p<phase.s-1e-10)return phase.light;p-=phase.s;}return 'red';}\nfunction advance(now){const target=(now-start)/1000;while(simulated<target){const dt=Math.min(1/240,target-simulated);const active=phaseAt(simulated+dt/2);const ordered=cars.slice().sort((a,b)=>b.x-a.x);for(let i=0;i<ordered.length;i++){const car=ordered[i];let limit=Infinity;if(car.x<325&&active\"'!=='\"'green')limit=324;if(i>0)limit=Math.min(limit,ordered[i-1].x-60);car.x=Math.max(car.x,Math.min(car.x+125*dt,limit));}for(const car of cars)if(car.x>780)car.x=-45;simulated+=dt;}light=phaseAt(target);}\nfunction draw(){ctx.clearRect(0,0,720,480);ctx.fillStyle='#86a776';ctx.fillRect(0,0,720,480);ctx.fillStyle='#51575b';ctx.fillRect(0,210,720,120);ctx.fillRect(355,0,110,480);ctx.strokeStyle='#d7d8c8';ctx.lineWidth=3;ctx.setLineDash([22,18]);ctx.beginPath();ctx.moveTo(0,270);ctx.lineTo(720,270);ctx.stroke();ctx.setLineDash([]);ctx.fillStyle='#fff';ctx.fillRect(325,275,4,49);ctx.fillStyle='#353a3c';ctx.fillRect(332,130,7,78);ctx.fillStyle='#20262b';ctx.fillRect(315,66,40,106);for(let i=0;i<3;i++){const on=i===0?(light==='red'||light==='red_amber'):i===1?(light==='amber'||light==='red_amber'):light==='green';ctx.beginPath();ctx.arc(335,85+i*33,12,0,Math.PI*2);ctx.fillStyle=on?['#ff3838','#ffc531','#38f06b'][i]:'#41484a';ctx.fill();}for(const car of cars){ctx.fillStyle='#20252a';ctx.fillRect(car.x-38,283,10,34);ctx.fillRect(car.x-14,283,9,34);ctx.fillStyle=car.color;ctx.fillRect(car.x-44,286,44,28);ctx.fillStyle='#c4e4ec';ctx.fillRect(car.x-16,289,9,22);ctx.fillStyle='#fff3c2';ctx.fillRect(car.x-3,289,3,5);ctx.fillRect(car.x-3,306,3,5);}ctx.fillStyle='#20372c';ctx.font='18px sans-serif';ctx.fillText('Junction 511',24,36);}\nwindow.sceneState=function(){advance(performance.now());draw();return {light,cars:cars.map(car=>({x:car.x}))};};\nfunction frame(now){advance(now);draw();requestAnimationFrame(frame);}draw();requestAnimationFrame(frame);\n</script></body></html>\nEOF\nwc -c out/scene.html\"", "aggregated_output": "2655 out/scene.html\n", "exit_code": 0, "status": "completed"}}
{"type": "item.completed", "item": {"id": "item_3", "type": "agent_message", "text": "Created [out/scene.html](out/scene.html) with the specified stage, timed light sequence, three animated cars, and `window.sceneState()`. All assets are inline for offline use."}}
{"type": "turn.completed", "usage": {"input_tokens": 48392, "cached_input_tokens": 44544, "cache_write_input_tokens": 0, "output_tokens": 1371, "reasoning_output_tokens": 290}}